Global biosphere–climate interaction: a causal appraisal of observations and models over multiple temporal scales

Claessen, Jeroen; Molini, Annalisa; Martens, Brecht; Detto, Matteo; Demuzere, Matthias; Miralles, Diego G.

doi:https://doi.org/10.5194/bg-16-4851-2019

Articles | Volume 16, issue 24

https://doi.org/10.5194/bg-16-4851-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-16-4851-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 24

Research article

|

20 Dec 2019

Research article |

| 20 Dec 2019

Global biosphere–climate interaction: a causal appraisal of observations and models over multiple temporal scales

Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles

Model code and software

Source code: Conditional spectral Granger causality J. Claessen, A. Molini, B. Martens, M. Detto, M. Demuzere, and D. G. Miralles https://github.com/lhwm/ConditionalSpectralGrangerCausality

Short summary

Bidirectional interactions between vegetation and climate are unraveled over short (monthly) and long (inter-annual) temporal scales. Analyses use a novel causal inference method based on wavelet theory. The performance of climate models at representing these interactions is benchmarked against satellite data. Climate models can reproduce the overall climate controls on vegetation at all temporal scales, while their performance at representing biophysical feedbacks on climate is less adequate.